Category Archives: Analytics Education

Difference between RF(M) Scores & LifeCycle Grids?

Analytical Culture, Analytics Education, DataBase Marketing, Driller Q & A, Measuring Engagement, ,

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

Both RF(M) scoring and Lifecycle Grids use the same key predictive metrics – Recency and Frequency. So what’s the difference? RFM is a predictive “snapshot” at a specific point in time; LifeCycle Grids are more like a “movie” designed to be predictive over different periods of time. Another way to think of this: RFM is tactical, LifeCycle Grids are strategic.

You dig? Let’s Drill …

Q:  We’re a telecom company trying to get a handle on customer churn and defection, so we can come up with some programs that will hopefully extend customer participation.  We live in the no contract space, offering a service that’s an add on to wireless phone service, so we don’t have a good indicator as to when the customer relationship might end.

A:  Ah, yes.  Your business model is “built for churn”, as I said on my blog the other day.  The behavior then is more like retail, where independent decisions are made in an ongoing way, deciding again and again to purchase.

Q:  I think your LifeCycle Grids method will show best what is happening to our customers.  If using this method, there doesn’t seem to be any reason to do the RF scoring as customers are just going into cells based on where they fall in the Recency and Frequency spectrum.  Is that correct?  Is there any real  difference between RF scoring and the LifeCycle Grids approach?

A:  You are partially correct, they are two versions of the same idea – both are scoring using Recency and Frequency. The traditional RF(M) scoring where customers are ranked against each other is a “relative” scoring method used primarily for campaigns – it is tactical, an allocation of resources model. 

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Problems Calculating Retention Rate

Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

What is your customer retention rate? Well, that kinda depends on how you define the customer. Have you had an internal discussion, and more importantly, solidified agreement across divisions / functions on the definition of an (active?) customer? Please do.

For example, is someone who hasn’t interacted with your company in any way for over 5 years still a customer? You see, if you don’t specifically define a customer, then you can’t have discussions around topics like reactivation, retention, Lifetime Value (LTV) and so forth. Where to start? With segmentation. Create segments of similar customers, then try to decide which segments are still customers; this exercise will get you going down the right track. The Drillin’?

Q:  Seasonality has great effects on customers’ purchasing activities in the retailing industry, as you may easily understand.

A:  Yes…

Q:  Furthermore, what you call Latency has also great effects on their purchasing activities, (I mean, for example, the customer who purchased a coat in one winter season are not expected to purchase another until the next winter season and so forth.)

A:  Yes, but you are profiling customers, not products, right?  The customer who bought the coat may also buy a dress, shoes, pants in other seasons?  Your approach so far sounds a bit too product centric…

Q:  Here is the problem, how these issues of seasonality and Latency must be taken into consideration for calculating retention rate?

A:  Well, you can take it into account or not, depending on your objectives.  What is the objective of the analysis?  If the objective means you should take these issues into account, then you probably should segment the customer base to do so.

Continue reading Problems Calculating Retention Rate →

Creating Effective Retention Campaigns

Analytical Culture, Analytics Education, Customer Experience, DataBase Marketing, Driller Q & A, Marketing thru Operations, Measuring Engagement,

Jim answers questions from fellow Drillers
(More questions with answers here, Work Overview here, Index of concepts here)

Topic Overview

Hi again folks, Jim Novo here.

Have you ever offered a $100 off coupon to a new retail customer? I have. And guess what? There was no response, even though the average order size across all customers was $38!

So how is this kind of situation possible? Some products attract customers that are only interested in that product, and they are not going to buy again – period. Knowing this, the question for you: is this the kind of product you want to constantly feature / promote?

Guess that depends on the Drillin’, eh? Let’s get to it …

Creating Effective Retention Campaigns

Q:  Hi Jim,

Love your newsletters.  Do you have a tip jar I can use to donate to the cause?

A:  Hmmm…maybe I ought to start one…nah.  It all works out in the end!

Q:  Take a look at this chart I did of cumulative customer purchase Recency (actual numbers changed but the relationships are same): See below for explanation **

** Jim’s Note: How to read the chart:

“In the past 3 months, (“3” on horizontal axis), 30% of our customers have made a purchase (“30%” on vertical axis).  In the past 7 months, almost 40% of our customers have made a purchase.  Because the last category is “last purchase 36 months ago or longer”, the chart includes all customers – 100%.  

Since each customer can have only 1 “most Recent” purchase, each customer is on the chart only once.  Therefore, if 40% of customers have made a purchase in the past 7 months, 60% have not made a purchase.

Q:  What does this pattern (the % of total by group) tell one generally about the attrition in the business model?  It’s interesting, I’ve never looked at this kind of diagram before.  For our business (wine retailer with “club” option), I generally consider anyone with a transaction in the past 12 months to still be a customer.

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